WIT Press


Outlier Detection In Financial Statements: A Text Mining Method

Price

Free (open access)

Paper DOI

10.2495/DATA090081

Volume

42

Pages

12

Page Range

71 - 82

Published

2009

Size

330 kb

Author(s)

S. S. Kamaruddin, A. R. Hamdan, A. Abu Bakar & F. Mat Nor

Abstract

This paper presents a text mining methodology to extract outlying knowledge from a collection of financial statements. The main idea is to extract relevant financial performance indicators and discover implicit textual description of the indicators. The extracted information was represented using a network language i.e. conceptual graph. Outlier mining was performed on the conceptual graph representation using a deviation based method. Experiments were carried out to evaluate the effectiveness of the proposed method. Results show that the proposed method is able to excerpt outlying knowledge from the financial statements with accuracy comparable to human experts. Keywords: text mining, information extraction, conceptual graphs, outlier mining in text, deviation based outlier mining method.

Keywords

text mining, information extraction, conceptual graphs, outlier mining in text, deviation based outlier mining method.